Multidisciplinary Optimization of a Radial Compressor for Microgas Turbine Applications

Abstract

A multidisciplinary optimization system and its application to the design of a small radial compressor impeller are presented. The method uses a genetic algorithm and artificial neural network to find a compromise between the conflicting demands of high efficiency and low centrifugal stresses in the blades. Concurrent analyses of the aero performance and stress predictions replace the traditional time consuming sequential design approach. The aerodynamic performance, predicted by a 3D Navier–Stokes solver, is maximized while limiting the mechanical stresses to a maximum value. The stresses are calculated by means of a finite element analysis, and controlled by modifying the blade camber, lean, and thickness at the hub. The results show that it is possible to obtain a significant reduction of the centrifugal stresses in the blades without penalizing the performance.